The Path to Net-Zero in Dairy Production: Are Pronounced Decreases in Enteric Methane Achievable?
Bibliographic record
Abstract
Achieving net-zero greenhouse gas (GHG) emissions in dairy production will require >50% reduction in enteric methane (CH 4 ) emissions together with elimination of emissions from feed production, additional carbon sequestration, reduction in manure emissions, anaerobic digestion of manure, and decreased reliance on fossil fuel energy. Over past decades, improved production efficiency has reduced GHG intensity of milk production (i.e., emissions per unit of milk) in the United States, but this trend can continue only if cows are bred for increased efficiency. Genetic selection of low-CH 4 -producing animals, diet reformulation, use of feed additives, and vaccination show tremendous potential for enteric CH 4 mitigation; however, few mitigation strategies are currently available, and added cost without increased revenue is a major barrier to implementation. Complete elimination of CH 4 emissions from dairying is likely not possible without negatively affecting milk production; thus, offsets and removals of other GHGs will be needed to achieve net-zero milk production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".